Table 11.

Spline regression around the event day for South China Provinces.

	Main Board				SME/GEM
	Coef.	SE	t-Stat		Coef.	SE	t-Stat
	(×100)	(×100)			(×100)	(×100)
${\Phi }_{10}$	−3.60	0.74	−4.84		−3.51	0.81	−4.33
${\Phi }_9$	−0.44	0.47	−0.96		0.50	0.56	0.89
${\Phi }_8$	−1.52	0.46	−3.30		−0.47	0.59	−0.79
${\Phi }_7$	0.43	0.38	1.12		0.90	0.44	2.05
${\Phi }_6$	−0.26	0.19	−1.42		0.86	0.75	1.14
${\Phi }_5$	−0.64	0.16	−4.01		−0.07	0.20	−0.35
${\Phi }_4$	−0.81	0.35	−2.31		−0.70	0.43	−1.62
${\Phi }_3$	−0.19	0.16	−1.20		0.01	0.33	0.04
${\Phi }_2$	0.66	0.20	3.31		1.02	0.21	4.80
${\Phi }_1$	−0.11	0.16	−0.73		−0.08	0.26	−0.31
$\beta \left(COVI{D}_{it}\right)$	−6.77	1.27	−5.31		−6.94	1.40	−4.95
${\Psi }_1$	−0.11	0.40	−0.29		0.73	0.41	1.79
${\Psi }_2$	2.19	0.16	13.33		2.99	0.21	14.58
${\Psi }_3$	2.01	0.25	8.08		2.90	0.18	16.00
${\Psi }_4$	0.98	0.08	12.46		1.88	0.15	12.81
${\Psi }_5$	1.10	0.29	3.75		0.68	0.39	1.73
${\Psi }_6$	−0.13	0.22	−0.61		−0.40	0.47	−0.84
${\Psi }_7$	1.18	0.31	3.79		2.08	0.35	6.03
${\Psi }_8$	−0.63	0.24	−2.60		−0.70	0.28	−2.50
${\Psi }_9$	0.55	0.36	1.53		0.48	0.42	1.12
${\Psi }_{10}$	2.47	0.31	7.84		3.63	0.34	10.71
Industry Fixed Effect	Yes				Yes
Province Fixed Effect	Yes				Yes
Adj. R-square (%)	3.32				1.85

Note: This table reports the spline regression results of the coefficient estimates, standard errors (SE), $t$-statistics (t-stat) and adjusted R-square. Standard errors are grouped at the province level. The regression specification is shown in Eq. (3). We use the daily return series of Main Board firms and SME/GEM firms in the South China provinces as the dependent variable. The sample period is between Dec. 2th, 2019 and Feb 23rd, 2020.